Publication Bibtex

NILC_USP: Aspect Extraction using Semantic Labels (bibtex)
by Balage Filho, Pedro Paulo; Pardo, Thiago Alexandre Salgueiro
Abstract:
This paper details the system NILC USP that participated in the Semeval 2014: Aspect Based Sentiment Analysis task. This system uses a Conditional Random Field (CRF) algorithm for extracting the aspects mentioned in the text. Our work added semantic labels into a basic feature set for measuring the efficiency of those for aspect extraction. We used the semantic roles and the highest verb frame as features for the machine learning. Overall, our results demonstrated that the system could not improve with the use of this semantic information, but its precision was increased.

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Reference:
P. P. Balage Filho, T. A. S. Pardo, "NILC_USP: Aspect Extraction using Semantic Labels", in Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), P. Nakov, T. Zesch, Eds., Dublin, Ireland: Association for Computational Linguistics and Dublin City University, 2014, pp. 433-436.
Bibtex Entry:
@InProceedings{BalageFilho2014NILCUSPAspectExtraction,
  Title                    = {{NILC\_USP}: Aspect Extraction using Semantic Labels},
  Author                   = {Balage Filho, Pedro Paulo and Pardo, Thiago Alexandre Salgueiro},
  Booktitle                = {Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)},
  Year                     = {2014},

  Address                  = {Dublin, Ireland},
  Editor                   = {Nakov, Preslav and Zesch, Torsten},
  Month                    = {23--24~} # aug,
  Pages                    = {433--436},
  Publisher                = {Association for Computational Linguistics and Dublin City University},

  Abstract                 = {This paper details the system NILC USP that participated in the Semeval 2014: Aspect Based Sentiment Analysis task. This system uses a Conditional Random Field (CRF) algorithm for extracting the aspects mentioned in the text. Our work added semantic labels into a basic feature set for measuring the efficiency of those for aspect extraction. We used the semantic roles and the highest verb frame as features for the machine learning. Overall, our results demonstrated that the system could not improve with the use of this semantic information, but its precision was increased.},
  PDF                      = {http://www.pedrobalage.com/pubs/BalageFilho2014NILCUSPAspectExtraction.pdf},
  Poster                   = {http://www.pedrobalage.com/pubs/BalageFilho2014NILCUSPAspectExtractionPoster.pdf},
  SourceCode               = {https://github.com/pedrobalage/SemevalAspectBasedSentimentAnalysis}
}
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